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Record W3144034678 · doi:10.1139/cjfr-2020-0475

Regional variation in wood discoloration in paper birch trees

2021· article· en· W3144034678 on OpenAlexafffundvenueabout
Guillaume Giroud, Emmanuel Duchâteau, Isabelle Auger, Filip Havreljuk, Julie Barrette

Bibliographic record

VenueCanadian Journal of Forest Research · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsMinistère des Ressources naturelles et des Forêts
FundersMinistère des Forêts, de la Faune et des Parcs
KeywordsBasal areaYellow birchBetula pendulaBetulaceaeMarshForestryBotanyEnvironmental scienceHardwoodGeographyHorticultureBiologyEcologyWetland

Abstract

fetched live from OpenAlex

Wood discoloration was investigated in 721 paper birch (Betula papyrifera Marsh.) trees from 146 sites across the managed forest of Quebec, Canada. Discoloration was present at breast height in 85% of trees, but its impact was limited in terms of tree basal area and volume, with proportions of 6.4% and 3.6%, respectively. These two measures were strongly correlated. Discoloration changed wood appearance but had no effect on microfibril angle and modulus of elasticity. Discolored wood was nevertheless denser, probably due to the accumulation of colored extractives. Predictive models were also developed and applied to 415 711 paper birch trees from 51 689 inventory plots across the province to study regional variation. Higher proportions of discolored basal area were obtained in the southwestern areas of the province where the climate is warmer and drier. In these areas, paper birch trees are larger and, likely, support bigger branches which can cause larger columns of discoloration when broken. Lower proportions were found in eastern regions where snowfall is more abundant. Smaller, more flexible branches in trees growing in these areas could explain this result. This study confirms the feasibility of large-scale mapping of wood discoloration in standing trees based on forest inventory and climate data.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.061
GPT teacher head0.303
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2021
Admission routes4
Has abstractyes

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